Fairness Under Distribution Shift: From Static Metrics to Adaptive Assurance

Authors

  • Sean Bryant Author

Keywords:

algorithmic fairness, distribution shift, robustness, monitoring, responsible AI

Abstract

This review examines fairness assurance under distribution shift. The organizing question is how fairness evidence should be updated when populations, labels, incentives, or deployment conditions change. Ten related scholarly sources are synthesized through a decision-centered framework spanning problem definition, mechanism, measurement, evaluation, implementation, and governance. The review does not invent experiments, pooled estimates, or unreported quantitative results. It instead evaluates the strength and transferability of the available evidence, with particular attention to assuming a fairness metric remains valid after the data-generating process changes. The resulting framework links technical or empirical performance to explicit use conditions and identifies tests that should precede wider adoption in high-impact predictive and ranking systems.

References

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Published

2026-08-08

Issue

Vol. 1 (2026): Archive

Section

Review

License

Copyright (c) 2026 Journal of Algorithmic Discovery and Applied AI

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This work is licensed under a Creative Commons Attribution 4.0 International License.